SHADE - A Value-Oriented Approach to Managing Software Asset Degradation

This research focuses on raising awareness of Asset Value degradation, which is a consequence of suboptimal decisions made due to the need for speed in today's software development environments. We aim to move a step forward from the Technical Debt (TD) concept which is too limiting as it does not cover the chronological perspective of degradation or spillover effects of suboptimal design from one asset to other technical, as well as non-technical (e.g. process), assets.

Aim of the project

The results of the Shade project aim to raise awareness of the degradation of assets, so that companies can take this degradation into account when budgeting for development activities and to provide decision support for planning degradation mitigation activities with a focus on value creation.

Implementation

One of the main objectives is to provide companies with a framework as a means to measure, monitor and predict the degradation of their software assets with the same accuracy as the models they use to predict revenues.

The project also includes empirical studies that will allow practitioners to understand how asset value and its degradation evolve over the project lifecycle.

The results will help to gain understanding, through the empirical evidence, of the types of asset degradation that act as barriers to firms' value creation capabilities. But also how this degradation can be tracked and mitigated in an effective way.

The main contribution and thus outcome of this project is to define an asset management framework, also supported by tools, that will allow software development organizations to measure and monitor asset value as well as predict, manage and mitigate its deterioration.

Financier: KK-stiftelsen

Status: Ended

Area: Software Engineering

Project start: 2018-01-01

Project end: 2020-12-31

Contact person: Javier Gonzalez Huerta

Project partner: Ericsson, Spotify

Project manager
Javier Gonzalez Huerta

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Participants
Tony Gorschek

Professor

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Ehsan Zabardast

Adjunct Senior Lecturer

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Kai Petersen

Affiliated Professor

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